Enhanced Image Processing and Fuzzy Logic Approach for Optimizing Driver Drowsiness Detection

Abstract

Driver drowsiness is a severe problem that usually causes traffic accidents, classified as more dangerous. The record of the National Safety Council reported that drowsy driving is caused by 9.5% of all crashes (100,000 cases). Therefore, preventing and minimizing driver fatigue is a significant research area. This study aims to design a nonintrusive real-time drowsiness system based on image processing and fuzzy logic techniques. It is an enhanced approach for Viola–Jones to examine different visual signs to detect the driver's drowsiness level. It extracted eye blink duration and mouth features to detect driver drowsiness based on the desired facial feature image in a specific driver video frame. The size and orientation of the captured features were tracked and handled for determining image features such as brightness, shadows, and clearness. Lastly, the fuzzy control system provides different alert sounds based on the tracked information from the face, eyes, and mouth in separate cases, such as race, wearing glasses or not, gender, and various illumination backgrounds. The experiments’ results show that the proposed approach achieved high accuracy of 94.5% in detecting driver status compared with other studies. Also, the fuzzy logic controller efficiently issued the required alert signal of the drowsy driver status that helps to save the driver's life.